Recovering 3D Basin Basement Relief Using High-Precision Magnetic Data Through Particle Swarm Optimization and Back Propagation Algorithm

The Inversion of magnetic basement interfaces in basins is critical for interpreting potential field data and studying geothermal resource distribution as well as basin formation and evolution. This paper proposes a new method for the inversion of magnetic basement interfaces using a particle swarm...

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Bibliographic Details
Published in:IEEE access Vol. 13; pp. 44343 - 44353
Main Authors: Yan, Shen, Zhang, Xinjun, Shang, Zhongda, Wang, Kai, Ma, Yixin
Format: Journal Article
Language:English
Published: IEEE 2025
Subjects:
Data models
Feature extraction
Machine learning
Machine learning algorithms
Magnetic domains
Magnetic field measurement
Magnetic substrate interface
numerical simulation
Particle swarm optimization
potential field processing conversion
PSO-BP neural network regression algorithm
Reliability
Solid modeling
Training
ISSN:2169-3536, 2169-3536
Online Access:Get full text
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Summary:The Inversion of magnetic basement interfaces in basins is critical for interpreting potential field data and studying geothermal resource distribution as well as basin formation and evolution. This paper proposes a new method for the inversion of magnetic basement interfaces using a particle swarm optimization algorithm that combines potential field processing and machine learning techniques. This method generates magnetic base interface models and the corresponding magnetic anomaly data through the random midpoint displacement method and magnetic interface finite element forward simulation. These anomalies are then handled using techniques, such as directional transformations, analytical continuation, spatial derivatives, and fractional transformations. Feature attributes were extracted, and the Gini importance was used to quantify feature factor contributions, screen out effective features, and improve algorithm efficiency. Validity and practicality were verified through an analysis of the theoretical and noise models. The proposed machine learning-based method is more intelligent, efficient, and accurately reflects the undulations of magnetic-based interfaces. Application to magnetic survey data in the Datong Basin resulted in a reliable basin-based model that matched known structural information, thereby opening a new direction for magnetic interface inversion research.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2025.3546636